Detection and Removal of Moving Object Shadows Using Geometry and Color Information for Indoor Video Streams

Abstract

The detection and removal of moving object shadows is a challenging issue. In this article, we propose a new approach for accurately removing shadows on modern buildings in the presence of a moving object in the scene. Our approach is capable of achieving good performance when addressing multiple shadow problems, by reducing background surface similarity and ghost artifacts. First, a combined contrast enhancement technique is applied to the input frame sequences to produce high-quality output images for indoor surroundings with an artificial light source. After obtaining suitable enhanced images, segmentation and noise removal filtering are applied to create a foreground mask of the possible candidate moving object shadow regions. Subsequently, geometry and color information are utilized to remove detected shadow pixels that incorrectly include the foreground mask. Here, experiments show that our method correctly detects and removes shadowed pixels in object tracking tasks, such as in universities, department stores, or several indoor sports games.